def test_inference_emotion_recognition(self):
model = HubertForSequenceClassification.from_pretrained(
"superb/hubert-base-superb-er",
torch_dtype=torch.float16).to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained(
"superb/hubert-base-superb-er")
input_data = self._load_superb("er", 4)
inputs = processor(input_data["speech"],
return_tensors="pt",
padding=True)
input_values = inputs.input_values.half().to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
outputs = model(input_values, attention_mask=attention_mask)
predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)
expected_labels = [1, 1, 2, 2]
# s3prl logits for the same batch
expected_logits = torch.tensor([2.8384, 2.3389, 3.8564, 4.5558],
dtype=torch.float16,
device=torch_device)
self.assertListEqual(predicted_ids.tolist(), expected_labels)
# TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
self.assertTrue(
torch.allclose(predicted_logits, expected_logits, atol=1e-1))
def test_inference_speaker_identification(self):
model = HubertForSequenceClassification.from_pretrained(
"superb/hubert-base-superb-sid",
torch_dtype=torch.float16).to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained(
"superb/hubert-base-superb-sid")
input_data = self._load_superb("si", 4)
output_logits = []
with torch.no_grad():
for example in input_data["speech"]:
input = processor(example, return_tensors="pt", padding=True)
output = model(input.input_values.half().to(torch_device),
attention_mask=None)
output_logits.append(output.logits[0])
output_logits = torch.stack(output_logits)
predicted_logits, predicted_ids = torch.max(output_logits, dim=-1)
expected_labels = [5, 1, 1, 3]
# s3prl logits for the same batch
expected_logits = torch.tensor(
[78231.5547, 123166.6094, 122785.4141, 84851.2969],
dtype=torch.float16,
device=torch_device)
self.assertListEqual(predicted_ids.tolist(), expected_labels)
# TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
self.assertTrue(
torch.allclose(predicted_logits, expected_logits, atol=10))
def test_inference_keyword_spotting(self):
model = HubertForSequenceClassification.from_pretrained(
"superb/hubert-base-superb-ks",
torch_dtype=torch.float16).to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained(
"superb/hubert-base-superb-ks")
input_data = self._load_superb("ks", 4)
inputs = processor(input_data["speech"],
return_tensors="pt",
padding=True)
input_values = inputs.input_values.half().to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
outputs = model(input_values, attention_mask=attention_mask)
predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)
expected_labels = [2, 6, 10, 9]
# s3prl logits for the same batch
expected_logits = torch.tensor([7.6692, 17.7795, 11.1562, 11.8232],
dtype=torch.float16,
device=torch_device)
self.assertListEqual(predicted_ids.tolist(), expected_labels)
self.assertTrue(
torch.allclose(predicted_logits, expected_logits, atol=2e-2))
def test_inference_intent_classification(self):
model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-ic").to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ic")
input_data = self._load_superb("ic", 4)
inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
outputs = model(input_values, attention_mask=attention_mask)
predicted_logits_action, predicted_ids_action = torch.max(outputs.logits[:, :6], dim=-1)
predicted_logits_object, predicted_ids_object = torch.max(outputs.logits[:, 6:20], dim=-1)
predicted_logits_location, predicted_ids_location = torch.max(outputs.logits[:, 20:24], dim=-1)
expected_labels_action = [1, 0, 4, 3]
expected_logits_action = torch.tensor([5.9052, 12.5865, 4.4840, 10.0240], device=torch_device)
expected_labels_object = [1, 10, 3, 4]
expected_logits_object = torch.tensor([5.5316, 11.7946, 8.1672, 23.2415], device=torch_device)
expected_labels_location = [0, 0, 0, 1]
expected_logits_location = torch.tensor([5.2053, 8.9577, 10.0447, 8.1481], device=torch_device)
self.assertListEqual(predicted_ids_action.tolist(), expected_labels_action)
self.assertListEqual(predicted_ids_object.tolist(), expected_labels_object)
self.assertListEqual(predicted_ids_location.tolist(), expected_labels_location)
# TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
self.assertTrue(torch.allclose(predicted_logits_action, expected_logits_action, atol=3e-1))
self.assertTrue(torch.allclose(predicted_logits_object, expected_logits_object, atol=3e-1))
self.assertTrue(torch.allclose(predicted_logits_location, expected_logits_location, atol=3e-1))
def check_seq_classifier_loss(self, config, input_values, *args):
model = HubertForSequenceClassification(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape,
device=torch_device,
dtype=torch.long)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1),
len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i]:] = 0.0
attention_mask[i, input_lengths[i]:] = 0
masked_loss = model(input_values,
attention_mask=attention_mask,
labels=labels).loss.item()
unmasked_loss = model(input_values, labels=labels).loss.item()
self.parent.assertTrue(isinstance(masked_loss, float))
self.parent.assertTrue(isinstance(unmasked_loss, float))
self.parent.assertTrue(masked_loss != unmasked_loss)
def check_seq_classifier_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = HubertForSequenceClassification(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
model.freeze_base_model()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
